R Final Project¶

Monish Tammareddy - mt575¶

Narella Vamshi - nv345¶

Saksham Jain - sj764¶

Importing all the required Libraries¶

In [123]:
import pandas as pd_rp
import numpy as np_rp
import matplotlib.pyplot as plt_rp
import seaborn as sns_rp
import plotly.express as px_rp
import math
from scipy.stats import zscore
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Lasso
from sklearn.linear_model import Ridge
from sklearn.svm import SVR
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import mean_squared_error
from xgboost import XGBRegressor
import warnings
pd_rp.set_option('display.max_columns', None)
warnings.filterwarnings("ignore")

We will be using regression algorithms as it is a price prediction problem and we know that in these cases we use Regression algorithms¶

Loading Training Dataset¶

In [124]:
df_rp = pd_rp.read_csv('/Users/narellavamshi/Downloads/train.csv',index_col='Id')
df_rp_t= pd_rp.read_csv('/Users/narellavamshi/Downloads/test.csv',index_col='Id')
df_rp.head()
Out[124]:
MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour Utilities LotConfig LandSlope Neighborhood Condition1 Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF Heating HeatingQC CentralAir Electrical 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr KitchenAbvGr KitchenQual TotRmsAbvGrd Functional Fireplaces FireplaceQu GarageType GarageYrBlt GarageFinish GarageCars GarageArea GarageQual GarageCond PavedDrive WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold SaleType SaleCondition SalePrice
Id
1 60 RL 65.0 8450 Pave NaN Reg Lvl AllPub Inside Gtl CollgCr Norm Norm 1Fam 2Story 7 5 2003 2003 Gable CompShg VinylSd VinylSd BrkFace 196.0 Gd TA PConc Gd TA No GLQ 706 Unf 0 150 856 GasA Ex Y SBrkr 856 854 0 1710 1 0 2 1 3 1 Gd 8 Typ 0 NaN Attchd 2003.0 RFn 2 548 TA TA Y 0 61 0 0 0 0 NaN NaN NaN 0 2 2008 WD Normal 208500
2 20 RL 80.0 9600 Pave NaN Reg Lvl AllPub FR2 Gtl Veenker Feedr Norm 1Fam 1Story 6 8 1976 1976 Gable CompShg MetalSd MetalSd NaN 0.0 TA TA CBlock Gd TA Gd ALQ 978 Unf 0 284 1262 GasA Ex Y SBrkr 1262 0 0 1262 0 1 2 0 3 1 TA 6 Typ 1 TA Attchd 1976.0 RFn 2 460 TA TA Y 298 0 0 0 0 0 NaN NaN NaN 0 5 2007 WD Normal 181500
3 60 RL 68.0 11250 Pave NaN IR1 Lvl AllPub Inside Gtl CollgCr Norm Norm 1Fam 2Story 7 5 2001 2002 Gable CompShg VinylSd VinylSd BrkFace 162.0 Gd TA PConc Gd TA Mn GLQ 486 Unf 0 434 920 GasA Ex Y SBrkr 920 866 0 1786 1 0 2 1 3 1 Gd 6 Typ 1 TA Attchd 2001.0 RFn 2 608 TA TA Y 0 42 0 0 0 0 NaN NaN NaN 0 9 2008 WD Normal 223500
4 70 RL 60.0 9550 Pave NaN IR1 Lvl AllPub Corner Gtl Crawfor Norm Norm 1Fam 2Story 7 5 1915 1970 Gable CompShg Wd Sdng Wd Shng NaN 0.0 TA TA BrkTil TA Gd No ALQ 216 Unf 0 540 756 GasA Gd Y SBrkr 961 756 0 1717 1 0 1 0 3 1 Gd 7 Typ 1 Gd Detchd 1998.0 Unf 3 642 TA TA Y 0 35 272 0 0 0 NaN NaN NaN 0 2 2006 WD Abnorml 140000
5 60 RL 84.0 14260 Pave NaN IR1 Lvl AllPub FR2 Gtl NoRidge Norm Norm 1Fam 2Story 8 5 2000 2000 Gable CompShg VinylSd VinylSd BrkFace 350.0 Gd TA PConc Gd TA Av GLQ 655 Unf 0 490 1145 GasA Ex Y SBrkr 1145 1053 0 2198 1 0 2 1 4 1 Gd 9 Typ 1 TA Attchd 2000.0 RFn 3 836 TA TA Y 192 84 0 0 0 0 NaN NaN NaN 0 12 2008 WD Normal 250000
In [125]:
df_rp_t.head()
Out[125]:
MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour Utilities LotConfig LandSlope Neighborhood Condition1 Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF Heating HeatingQC CentralAir Electrical 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr KitchenAbvGr KitchenQual TotRmsAbvGrd Functional Fireplaces FireplaceQu GarageType GarageYrBlt GarageFinish GarageCars GarageArea GarageQual GarageCond PavedDrive WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold SaleType SaleCondition
Id
1461 20 RH 80.0 11622 Pave NaN Reg Lvl AllPub Inside Gtl NAmes Feedr Norm 1Fam 1Story 5 6 1961 1961 Gable CompShg VinylSd VinylSd NaN 0.0 TA TA CBlock TA TA No Rec 468.0 LwQ 144.0 270.0 882.0 GasA TA Y SBrkr 896 0 0 896 0.0 0.0 1 0 2 1 TA 5 Typ 0 NaN Attchd 1961.0 Unf 1.0 730.0 TA TA Y 140 0 0 0 120 0 NaN MnPrv NaN 0 6 2010 WD Normal
1462 20 RL 81.0 14267 Pave NaN IR1 Lvl AllPub Corner Gtl NAmes Norm Norm 1Fam 1Story 6 6 1958 1958 Hip CompShg Wd Sdng Wd Sdng BrkFace 108.0 TA TA CBlock TA TA No ALQ 923.0 Unf 0.0 406.0 1329.0 GasA TA Y SBrkr 1329 0 0 1329 0.0 0.0 1 1 3 1 Gd 6 Typ 0 NaN Attchd 1958.0 Unf 1.0 312.0 TA TA Y 393 36 0 0 0 0 NaN NaN Gar2 12500 6 2010 WD Normal
1463 60 RL 74.0 13830 Pave NaN IR1 Lvl AllPub Inside Gtl Gilbert Norm Norm 1Fam 2Story 5 5 1997 1998 Gable CompShg VinylSd VinylSd NaN 0.0 TA TA PConc Gd TA No GLQ 791.0 Unf 0.0 137.0 928.0 GasA Gd Y SBrkr 928 701 0 1629 0.0 0.0 2 1 3 1 TA 6 Typ 1 TA Attchd 1997.0 Fin 2.0 482.0 TA TA Y 212 34 0 0 0 0 NaN MnPrv NaN 0 3 2010 WD Normal
1464 60 RL 78.0 9978 Pave NaN IR1 Lvl AllPub Inside Gtl Gilbert Norm Norm 1Fam 2Story 6 6 1998 1998 Gable CompShg VinylSd VinylSd BrkFace 20.0 TA TA PConc TA TA No GLQ 602.0 Unf 0.0 324.0 926.0 GasA Ex Y SBrkr 926 678 0 1604 0.0 0.0 2 1 3 1 Gd 7 Typ 1 Gd Attchd 1998.0 Fin 2.0 470.0 TA TA Y 360 36 0 0 0 0 NaN NaN NaN 0 6 2010 WD Normal
1465 120 RL 43.0 5005 Pave NaN IR1 HLS AllPub Inside Gtl StoneBr Norm Norm TwnhsE 1Story 8 5 1992 1992 Gable CompShg HdBoard HdBoard NaN 0.0 Gd TA PConc Gd TA No ALQ 263.0 Unf 0.0 1017.0 1280.0 GasA Ex Y SBrkr 1280 0 0 1280 0.0 0.0 2 0 2 1 Gd 5 Typ 0 NaN Attchd 1992.0 RFn 2.0 506.0 TA TA Y 0 82 0 0 144 0 NaN NaN NaN 0 1 2010 WD Normal
In [126]:
df_rp.tail()
Out[126]:
MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour Utilities LotConfig LandSlope Neighborhood Condition1 Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF Heating HeatingQC CentralAir Electrical 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr KitchenAbvGr KitchenQual TotRmsAbvGrd Functional Fireplaces FireplaceQu GarageType GarageYrBlt GarageFinish GarageCars GarageArea GarageQual GarageCond PavedDrive WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold SaleType SaleCondition SalePrice
Id
1456 60 RL 62.0 7917 Pave NaN Reg Lvl AllPub Inside Gtl Gilbert Norm Norm 1Fam 2Story 6 5 1999 2000 Gable CompShg VinylSd VinylSd NaN 0.0 TA TA PConc Gd TA No Unf 0 Unf 0 953 953 GasA Ex Y SBrkr 953 694 0 1647 0 0 2 1 3 1 TA 7 Typ 1 TA Attchd 1999.0 RFn 2 460 TA TA Y 0 40 0 0 0 0 NaN NaN NaN 0 8 2007 WD Normal 175000
1457 20 RL 85.0 13175 Pave NaN Reg Lvl AllPub Inside Gtl NWAmes Norm Norm 1Fam 1Story 6 6 1978 1988 Gable CompShg Plywood Plywood Stone 119.0 TA TA CBlock Gd TA No ALQ 790 Rec 163 589 1542 GasA TA Y SBrkr 2073 0 0 2073 1 0 2 0 3 1 TA 7 Min1 2 TA Attchd 1978.0 Unf 2 500 TA TA Y 349 0 0 0 0 0 NaN MnPrv NaN 0 2 2010 WD Normal 210000
1458 70 RL 66.0 9042 Pave NaN Reg Lvl AllPub Inside Gtl Crawfor Norm Norm 1Fam 2Story 7 9 1941 2006 Gable CompShg CemntBd CmentBd NaN 0.0 Ex Gd Stone TA Gd No GLQ 275 Unf 0 877 1152 GasA Ex Y SBrkr 1188 1152 0 2340 0 0 2 0 4 1 Gd 9 Typ 2 Gd Attchd 1941.0 RFn 1 252 TA TA Y 0 60 0 0 0 0 NaN GdPrv Shed 2500 5 2010 WD Normal 266500
1459 20 RL 68.0 9717 Pave NaN Reg Lvl AllPub Inside Gtl NAmes Norm Norm 1Fam 1Story 5 6 1950 1996 Hip CompShg MetalSd MetalSd NaN 0.0 TA TA CBlock TA TA Mn GLQ 49 Rec 1029 0 1078 GasA Gd Y FuseA 1078 0 0 1078 1 0 1 0 2 1 Gd 5 Typ 0 NaN Attchd 1950.0 Unf 1 240 TA TA Y 366 0 112 0 0 0 NaN NaN NaN 0 4 2010 WD Normal 142125
1460 20 RL 75.0 9937 Pave NaN Reg Lvl AllPub Inside Gtl Edwards Norm Norm 1Fam 1Story 5 6 1965 1965 Gable CompShg HdBoard HdBoard NaN 0.0 Gd TA CBlock TA TA No BLQ 830 LwQ 290 136 1256 GasA Gd Y SBrkr 1256 0 0 1256 1 0 1 1 3 1 TA 6 Typ 0 NaN Attchd 1965.0 Fin 1 276 TA TA Y 736 68 0 0 0 0 NaN NaN NaN 0 6 2008 WD Normal 147500

Dataset Shape and MetaData¶

In [127]:
df_rp.info()

<class 'pandas.core.frame.DataFrame'>
Index: 1460 entries, 1 to 1460
Data columns (total 80 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   MSSubClass     1460 non-null   int64  
 1   MSZoning       1460 non-null   object 
 2   LotFrontage    1201 non-null   float64
 3   LotArea        1460 non-null   int64  
 4   Street         1460 non-null   object 
 5   Alley          91 non-null     object 
 6   LotShape       1460 non-null   object 
 7   LandContour    1460 non-null   object 
 8   Utilities      1460 non-null   object 
 9   LotConfig      1460 non-null   object 
 10  LandSlope      1460 non-null   object 
 11  Neighborhood   1460 non-null   object 
 12  Condition1     1460 non-null   object 
 13  Condition2     1460 non-null   object 
 14  BldgType       1460 non-null   object 
 15  HouseStyle     1460 non-null   object 
 16  OverallQual    1460 non-null   int64  
 17  OverallCond    1460 non-null   int64  
 18  YearBuilt      1460 non-null   int64  
 19  YearRemodAdd   1460 non-null   int64  
 20  RoofStyle      1460 non-null   object 
 21  RoofMatl       1460 non-null   object 
 22  Exterior1st    1460 non-null   object 
 23  Exterior2nd    1460 non-null   object 
 24  MasVnrType     588 non-null    object 
 25  MasVnrArea     1452 non-null   float64
 26  ExterQual      1460 non-null   object 
 27  ExterCond      1460 non-null   object 
 28  Foundation     1460 non-null   object 
 29  BsmtQual       1423 non-null   object 
 30  BsmtCond       1423 non-null   object 
 31  BsmtExposure   1422 non-null   object 
 32  BsmtFinType1   1423 non-null   object 
 33  BsmtFinSF1     1460 non-null   int64  
 34  BsmtFinType2   1422 non-null   object 
 35  BsmtFinSF2     1460 non-null   int64  
 36  BsmtUnfSF      1460 non-null   int64  
 37  TotalBsmtSF    1460 non-null   int64  
 38  Heating        1460 non-null   object 
 39  HeatingQC      1460 non-null   object 
 40  CentralAir     1460 non-null   object 
 41  Electrical     1459 non-null   object 
 42  1stFlrSF       1460 non-null   int64  
 43  2ndFlrSF       1460 non-null   int64  
 44  LowQualFinSF   1460 non-null   int64  
 45  GrLivArea      1460 non-null   int64  
 46  BsmtFullBath   1460 non-null   int64  
 47  BsmtHalfBath   1460 non-null   int64  
 48  FullBath       1460 non-null   int64  
 49  HalfBath       1460 non-null   int64  
 50  BedroomAbvGr   1460 non-null   int64  
 51  KitchenAbvGr   1460 non-null   int64  
 52  KitchenQual    1460 non-null   object 
 53  TotRmsAbvGrd   1460 non-null   int64  
 54  Functional     1460 non-null   object 
 55  Fireplaces     1460 non-null   int64  
 56  FireplaceQu    770 non-null    object 
 57  GarageType     1379 non-null   object 
 58  GarageYrBlt    1379 non-null   float64
 59  GarageFinish   1379 non-null   object 
 60  GarageCars     1460 non-null   int64  
 61  GarageArea     1460 non-null   int64  
 62  GarageQual     1379 non-null   object 
 63  GarageCond     1379 non-null   object 
 64  PavedDrive     1460 non-null   object 
 65  WoodDeckSF     1460 non-null   int64  
 66  OpenPorchSF    1460 non-null   int64  
 67  EnclosedPorch  1460 non-null   int64  
 68  3SsnPorch      1460 non-null   int64  
 69  ScreenPorch    1460 non-null   int64  
 70  PoolArea       1460 non-null   int64  
 71  PoolQC         7 non-null      object 
 72  Fence          281 non-null    object 
 73  MiscFeature    54 non-null     object 
 74  MiscVal        1460 non-null   int64  
 75  MoSold         1460 non-null   int64  
 76  YrSold         1460 non-null   int64  
 77  SaleType       1460 non-null   object 
 78  SaleCondition  1460 non-null   object 
 79  SalePrice      1460 non-null   int64  
dtypes: float64(3), int64(34), object(43)
memory usage: 923.9+ KB
In [128]:
df_rp.describe()
Out[128]:
MSSubClass LotFrontage LotArea OverallQual OverallCond YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 BsmtFinSF2 BsmtUnfSF TotalBsmtSF 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr KitchenAbvGr TotRmsAbvGrd Fireplaces GarageYrBlt GarageCars GarageArea WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea MiscVal MoSold YrSold SalePrice
count 1460.000000 1201.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1452.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1379.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000
mean 56.897260 70.049958 10516.828082 6.099315 5.575342 1971.267808 1984.865753 103.685262 443.639726 46.549315 567.240411 1057.429452 1162.626712 346.992466 5.844521 1515.463699 0.425342 0.057534 1.565068 0.382877 2.866438 1.046575 6.517808 0.613014 1978.506164 1.767123 472.980137 94.244521 46.660274 21.954110 3.409589 15.060959 2.758904 43.489041 6.321918 2007.815753 180921.195890
std 42.300571 24.284752 9981.264932 1.382997 1.112799 30.202904 20.645407 181.066207 456.098091 161.319273 441.866955 438.705324 386.587738 436.528436 48.623081 525.480383 0.518911 0.238753 0.550916 0.502885 0.815778 0.220338 1.625393 0.644666 24.689725 0.747315 213.804841 125.338794 66.256028 61.119149 29.317331 55.757415 40.177307 496.123024 2.703626 1.328095 79442.502883
min 20.000000 21.000000 1300.000000 1.000000 1.000000 1872.000000 1950.000000 0.000000 0.000000 0.000000 0.000000 0.000000 334.000000 0.000000 0.000000 334.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2.000000 0.000000 1900.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 2006.000000 34900.000000
25% 20.000000 59.000000 7553.500000 5.000000 5.000000 1954.000000 1967.000000 0.000000 0.000000 0.000000 223.000000 795.750000 882.000000 0.000000 0.000000 1129.500000 0.000000 0.000000 1.000000 0.000000 2.000000 1.000000 5.000000 0.000000 1961.000000 1.000000 334.500000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 5.000000 2007.000000 129975.000000
50% 50.000000 69.000000 9478.500000 6.000000 5.000000 1973.000000 1994.000000 0.000000 383.500000 0.000000 477.500000 991.500000 1087.000000 0.000000 0.000000 1464.000000 0.000000 0.000000 2.000000 0.000000 3.000000 1.000000 6.000000 1.000000 1980.000000 2.000000 480.000000 0.000000 25.000000 0.000000 0.000000 0.000000 0.000000 0.000000 6.000000 2008.000000 163000.000000
75% 70.000000 80.000000 11601.500000 7.000000 6.000000 2000.000000 2004.000000 166.000000 712.250000 0.000000 808.000000 1298.250000 1391.250000 728.000000 0.000000 1776.750000 1.000000 0.000000 2.000000 1.000000 3.000000 1.000000 7.000000 1.000000 2002.000000 2.000000 576.000000 168.000000 68.000000 0.000000 0.000000 0.000000 0.000000 0.000000 8.000000 2009.000000 214000.000000
max 190.000000 313.000000 215245.000000 10.000000 9.000000 2010.000000 2010.000000 1600.000000 5644.000000 1474.000000 2336.000000 6110.000000 4692.000000 2065.000000 572.000000 5642.000000 3.000000 2.000000 3.000000 2.000000 8.000000 3.000000 14.000000 3.000000 2010.000000 4.000000 1418.000000 857.000000 547.000000 552.000000 508.000000 480.000000 738.000000 15500.000000 12.000000 2010.000000 755000.000000

Data Cleaning¶

Dealing with missing values¶

In [129]:
sns_rp.heatmap(df_rp.isnull(), yticklabels=False, cbar=False, cmap='viridis')
Out[129]:
<Axes: ylabel='Id'>
In [130]:
df_rp.isna()
Out[130]:
MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour Utilities LotConfig LandSlope Neighborhood Condition1 Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF Heating HeatingQC CentralAir Electrical 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr KitchenAbvGr KitchenQual TotRmsAbvGrd Functional Fireplaces FireplaceQu GarageType GarageYrBlt GarageFinish GarageCars GarageArea GarageQual GarageCond PavedDrive WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold SaleType SaleCondition SalePrice
Id
1 False False False False False True False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False True False False False False False False False False False False False False False False True True True False False False False False False
2 False False False False False True False False False False False False False False False False False False False False False False False False True False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False True True True False False False False False False
3 False False False False False True False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False True True True False False False False False False
4 False False False False False True False False False False False False False False False False False False False False False False False False True False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False True True True False False False False False False
5 False False False False False True False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False True True True False False False False False False
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1456 False False False False False True False False False False False False False False False False False False False False False False False False True False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False True True True False False False False False False
1457 False False False False False True False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False True False True False False False False False False
1458 False False False False False True False False False False False False False False False False False False False False False False False False True False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False True False False False False False False False False
1459 False False False False False True False False False False False False False False False False False False False False False False False False True False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False True False False False False False False False False False False False False False False True True True False False False False False False
1460 False False False False False True False False False False False False False False False False False False False False False False False False True False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False False True False False False False False False False False False False False False False False True True True False False False False False False

1460 rows × 80 columns

Determining the action plan to deal with the NaN values¶

Calculating the ratio of the Nan Values and hence we can determine what to substitute instead of them or remove them if required¶

In [131]:
print("Ratio of NaN(Not a NULL) in each Column")
print("**********************************************")
nan_ratio_rp = df_rp.isna().mean() 
nan_ratio_rp = nan_ratio_rp[nan_ratio_rp > 0]

for col_rp, ratio_rp in nan_ratio_rp.items():
    print(f"{col_rp} : {ratio_rp:.2f}")

Ratio of NaN(Not a NULL) in each Column
**********************************************
LotFrontage : 0.18
Alley : 0.94
MasVnrType : 0.60
MasVnrArea : 0.01
BsmtQual : 0.03
BsmtCond : 0.03
BsmtExposure : 0.03
BsmtFinType1 : 0.03
BsmtFinType2 : 0.03
Electrical : 0.00
FireplaceQu : 0.47
GarageType : 0.06
GarageYrBlt : 0.06
GarageFinish : 0.06
GarageQual : 0.06
GarageCond : 0.06
PoolQC : 1.00
Fence : 0.81
MiscFeature : 0.96

Hence if the ratio is close to 1 then we have to surely take some action regarding that feature/column¶

Dropping some of the columns because either they have lot of NaN values or else if NaN values are replaced then it may lead to negative performance¶

In [132]:
df_rp.drop(columns=['MasVnrType','PoolQC','PoolArea','BsmtHalfBath','KitchenAbvGr','Utilities'],inplace=True)

Replacing the NaN values by specific parameters such as numbers, mean, mode etc.¶

In [133]:
df_rp['LotFrontage'] = df_rp['LotFrontage'].fillna(0)
df_rp['Alley'] = df_rp['Alley'].fillna("No Alley Acess")
df_rp['MasVnrArea'] = df_rp['MasVnrArea'].fillna(df_rp['MasVnrArea'].mean())
df_rp['LotFrontage'] = df_rp['LotFrontage'].fillna(0)
df_rp['BsmtQual'] = df_rp['BsmtQual'].fillna("No Basment")
df_rp['BsmtCond'] = df_rp['BsmtCond'].fillna("No Basment")
df_rp['BsmtExposure'] = df_rp['BsmtExposure'].fillna("No Basment")
df_rp['BsmtFinType1'] = df_rp['BsmtFinType1'].fillna("No Basment")
df_rp['BsmtFinType2'] = df_rp['BsmtFinType2'].fillna("No Basment")
df_rp['Electrical'] = df_rp['Electrical'].fillna(df_rp['Electrical'].mode()[0])
df_rp['FireplaceQu'] = df_rp['FireplaceQu'].fillna("No Fireplace")
df_rp['GarageType'] = df_rp['GarageType'].fillna("No Garage")
df_rp['GarageYrBlt'] = df_rp['GarageYrBlt'].fillna(0)
df_rp['GarageFinish'] = df_rp['GarageFinish'].fillna("No Garage")
df_rp['GarageQual'] = df_rp['GarageQual'].fillna("No Garage")
df_rp['GarageCond'] = df_rp['GarageCond'].fillna("No Garage")
df_rp['Fence'] = df_rp['Fence'].fillna("No Fence")
df_rp['MiscFeature'] = df_rp['MiscFeature'].fillna("No Miscellaneous Feature")

Removing Outliers¶

Here we are using zscore threshold as 3 because it covers around 99.73% of the data and rest we are considering as outliers¶

In [134]:
from scipy.stats import zscore

def remove_outlier_zscore_rp(df_rp, x, zscore_thres=3):
    df_rp[f'{x}_zscore'] = zscore(df_rp[f'{x}'])
    df_rp.drop(df_rp[(df_rp[f'{x}_zscore'] >= zscore_thres) | (df_rp[f'{x}_zscore'] <= -zscore_thres)].index, inplace=True)
    df_rp.drop(columns=[f'{x}_zscore'], inplace=True)

Removing the outliers using the above user defined function¶

In [135]:
columns_rps = df_rp.select_dtypes('number').columns.tolist()
for col_rp in columns_rps:
    remove_outlier_zscore_rp(df_rp, col_rp)
In [136]:
df_rp.info()

<class 'pandas.core.frame.DataFrame'>
Index: 1001 entries, 1 to 1457
Data columns (total 74 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   MSSubClass     1001 non-null   int64  
 1   MSZoning       1001 non-null   object 
 2   LotFrontage    1001 non-null   float64
 3   LotArea        1001 non-null   int64  
 4   Street         1001 non-null   object 
 5   Alley          1001 non-null   object 
 6   LotShape       1001 non-null   object 
 7   LandContour    1001 non-null   object 
 8   LotConfig      1001 non-null   object 
 9   LandSlope      1001 non-null   object 
 10  Neighborhood   1001 non-null   object 
 11  Condition1     1001 non-null   object 
 12  Condition2     1001 non-null   object 
 13  BldgType       1001 non-null   object 
 14  HouseStyle     1001 non-null   object 
 15  OverallQual    1001 non-null   int64  
 16  OverallCond    1001 non-null   int64  
 17  YearBuilt      1001 non-null   int64  
 18  YearRemodAdd   1001 non-null   int64  
 19  RoofStyle      1001 non-null   object 
 20  RoofMatl       1001 non-null   object 
 21  Exterior1st    1001 non-null   object 
 22  Exterior2nd    1001 non-null   object 
 23  MasVnrArea     1001 non-null   float64
 24  ExterQual      1001 non-null   object 
 25  ExterCond      1001 non-null   object 
 26  Foundation     1001 non-null   object 
 27  BsmtQual       1001 non-null   object 
 28  BsmtCond       1001 non-null   object 
 29  BsmtExposure   1001 non-null   object 
 30  BsmtFinType1   1001 non-null   object 
 31  BsmtFinSF1     1001 non-null   int64  
 32  BsmtFinType2   1001 non-null   object 
 33  BsmtFinSF2     1001 non-null   int64  
 34  BsmtUnfSF      1001 non-null   int64  
 35  TotalBsmtSF    1001 non-null   int64  
 36  Heating        1001 non-null   object 
 37  HeatingQC      1001 non-null   object 
 38  CentralAir     1001 non-null   object 
 39  Electrical     1001 non-null   object 
 40  1stFlrSF       1001 non-null   int64  
 41  2ndFlrSF       1001 non-null   int64  
 42  LowQualFinSF   1001 non-null   int64  
 43  GrLivArea      1001 non-null   int64  
 44  BsmtFullBath   1001 non-null   int64  
 45  FullBath       1001 non-null   int64  
 46  HalfBath       1001 non-null   int64  
 47  BedroomAbvGr   1001 non-null   int64  
 48  KitchenQual    1001 non-null   object 
 49  TotRmsAbvGrd   1001 non-null   int64  
 50  Functional     1001 non-null   object 
 51  Fireplaces     1001 non-null   int64  
 52  FireplaceQu    1001 non-null   object 
 53  GarageType     1001 non-null   object 
 54  GarageYrBlt    1001 non-null   float64
 55  GarageFinish   1001 non-null   object 
 56  GarageCars     1001 non-null   int64  
 57  GarageArea     1001 non-null   int64  
 58  GarageQual     1001 non-null   object 
 59  GarageCond     1001 non-null   object 
 60  PavedDrive     1001 non-null   object 
 61  WoodDeckSF     1001 non-null   int64  
 62  OpenPorchSF    1001 non-null   int64  
 63  EnclosedPorch  1001 non-null   int64  
 64  3SsnPorch      1001 non-null   int64  
 65  ScreenPorch    1001 non-null   int64  
 66  Fence          1001 non-null   object 
 67  MiscFeature    1001 non-null   object 
 68  MiscVal        1001 non-null   int64  
 69  MoSold         1001 non-null   int64  
 70  YrSold         1001 non-null   int64  
 71  SaleType       1001 non-null   object 
 72  SaleCondition  1001 non-null   object 
 73  SalePrice      1001 non-null   int64  
dtypes: float64(3), int64(31), object(40)
memory usage: 586.5+ KB
In [137]:
sns_rp.heatmap(df_rp.isnull(), yticklabels=False, cbar=False, cmap='viridis')
Out[137]:
<Axes: ylabel='Id'>

Now we can see that the heatmap has no marks as we have dealt with all the NaN or null values in the dataframe¶

In [138]:
df_rp.shape
Out[138]:
(1001, 74)
In [139]:
df_rp.describe()
Out[139]:
MSSubClass LotFrontage LotArea OverallQual OverallCond YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 BsmtFinSF2 BsmtUnfSF TotalBsmtSF 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath FullBath HalfBath BedroomAbvGr TotRmsAbvGrd Fireplaces GarageYrBlt GarageCars GarageArea WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch MiscVal MoSold YrSold SalePrice
count 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000 1001.000000
mean 55.024975 55.664336 9289.864136 6.053946 5.524476 1975.368631 1985.549451 84.297814 416.560440 22.208791 568.288711 1007.057942 1103.766234 322.947053 0.292707 1427.005994 0.381618 1.565435 0.369630 2.812188 6.278721 0.546454 1979.672328 1.810190 479.136863 88.885115 39.817183 13.250749 0.118881 5.208791 14.839161 6.248751 2007.814186 171110.950050
std 39.801688 31.676193 3830.783887 1.253430 0.985723 27.635864 20.522519 132.001834 397.369021 84.511924 427.616819 365.825514 319.603944 404.584550 4.682226 406.913091 0.486027 0.515717 0.482946 0.688978 1.346565 0.595055 24.248598 0.589861 166.960134 106.565312 49.922235 40.553644 3.119432 26.049671 97.684672 2.668157 1.345154 57377.085862
min 20.000000 0.000000 1300.000000 2.000000 3.000000 1890.000000 1950.000000 0.000000 0.000000 0.000000 0.000000 0.000000 438.000000 0.000000 0.000000 438.000000 0.000000 0.000000 0.000000 1.000000 3.000000 0.000000 1910.000000 1.000000 160.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 2006.000000 35311.000000
25% 20.000000 40.000000 7252.000000 5.000000 5.000000 1957.000000 1968.000000 0.000000 0.000000 0.000000 239.000000 791.000000 864.000000 0.000000 0.000000 1113.000000 0.000000 1.000000 0.000000 2.000000 5.000000 0.000000 1962.000000 1.000000 352.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 4.000000 2007.000000 130000.000000
50% 50.000000 63.000000 9120.000000 6.000000 5.000000 1977.000000 1995.000000 0.000000 398.000000 0.000000 484.000000 960.000000 1052.000000 0.000000 0.000000 1414.000000 0.000000 2.000000 0.000000 3.000000 6.000000 0.000000 1983.000000 2.000000 474.000000 0.000000 25.000000 0.000000 0.000000 0.000000 0.000000 6.000000 2008.000000 160000.000000
75% 70.000000 76.000000 11040.000000 7.000000 6.000000 2002.000000 2004.000000 143.000000 690.000000 0.000000 810.000000 1234.000000 1314.000000 703.000000 0.000000 1682.000000 1.000000 2.000000 1.000000 3.000000 7.000000 1.000000 2003.000000 2.000000 576.000000 168.000000 62.000000 0.000000 0.000000 0.000000 0.000000 8.000000 2009.000000 201000.000000
max 180.000000 152.000000 36500.000000 10.000000 8.000000 2009.000000 2010.000000 640.000000 1573.000000 532.000000 1869.000000 2136.000000 2136.000000 1427.000000 80.000000 2727.000000 1.000000 3.000000 1.000000 5.000000 10.000000 2.000000 2010.000000 3.000000 954.000000 441.000000 224.000000 192.000000 96.000000 170.000000 1300.000000 12.000000 2010.000000 354000.000000

Analysis and Visualization¶

In [140]:
col_rps = df_rp.columns
col_rps
Out[140]:
Index(['MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'Alley',
       'LotShape', 'LandContour', 'LotConfig', 'LandSlope', 'Neighborhood',
       'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'OverallQual',
       'OverallCond', 'YearBuilt', 'YearRemodAdd', 'RoofStyle', 'RoofMatl',
       'Exterior1st', 'Exterior2nd', 'MasVnrArea', 'ExterQual', 'ExterCond',
       'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1',
       'BsmtFinSF1', 'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF',
       'Heating', 'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF',
       '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'FullBath',
       'HalfBath', 'BedroomAbvGr', 'KitchenQual', 'TotRmsAbvGrd', 'Functional',
       'Fireplaces', 'FireplaceQu', 'GarageType', 'GarageYrBlt',
       'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual', 'GarageCond',
       'PavedDrive', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch',
       'ScreenPorch', 'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold',
       'SaleType', 'SaleCondition', 'SalePrice'],
      dtype='object')

Plotting the histogram for features vs y_count values¶

In [141]:
for column in col_rps:
    plt_rp.figure(figsize=(7, 5))
    df_rp[column].hist(color='#3498db') 
    plt_rp.title(f'Histogram for {column}', fontsize=11)
    plt_rp.xlabel(column, fontsize=9)
    plt_rp.ylabel('y_count', fontsize=9)
    plt_rp.show()
In [142]:
plt_rp.figure(figsize=(35, 35))

sns_rp.heatmap(
    df_rp.select_dtypes('number').corr(),  
    vmax=0.7, 
    annot=True,  
    square=True, 
    cmap='Oranges'  
)

plt_rp.show()
In [143]:
def plot_numeric_col_with_price_rp(ax_rp, x):
    scatter = ax_rp.scatter(x=df_rp[x], y=df_rp['SalePrice'], c=df_rp['SalePrice'], cmap='Blues', alpha=0.75, s=10)
    ax_rp.set(xlabel=x, ylabel='Sales Price', title=f'{x} vs Sale Price')
    plt_rp.colorbar(scatter, ax=ax_rp, label='SalePrice')

selected_columns_rp = [column for column in df_rp.select_dtypes('number').columns if df_rp[column].nunique() > 16]

num_rows_rp = math.ceil(len(selected_columns_rp) / 2)

fig, axes = plt_rp.subplots(num_rows_rp, 2, figsize=(16, 5 * num_rows_rp))
axes = axes.flatten()

for axlen, column in enumerate(selected_columns_rp):
    if axlen >= len(axes):
        break
    plot_numeric_col_with_price_rp(axes[axlen], column)

plt_rp.tight_layout()
plt_rp.show()
In [144]:
def box_plot_categorical_column_with_price(x, ax_rp):
    sns_rp.boxplot(x=x, y='SalePrice', data=df_rp, ax=ax_rp, palette='Blues')
    ax_rp.set_xlabel(x)
    ax_rp.set_ylabel('Sale Price')
    ax_rp.set_xticklabels(ax_rp.get_xticklabels(), rotation=46, ha='right')
    ax_rp.set_title(f'Box Plot for Sales Price vs {x}')

columns = df_rp.select_dtypes(exclude='number').columns.tolist()
for c in df_rp.select_dtypes('number').columns.tolist():
    if df_rp[f'{c}'].nunique() <= 16:
        columns.append(c)
        
num_columns = 3
num_rows = (len(columns) + num_columns - 1) // num_columns
fig, axes = plt_rp.subplots(num_rows, num_columns, figsize=(16, 6 * num_rows), sharey=True)
axes = axes.flatten()
for num, column in enumerate(columns):
    box_plot_categorical_column_with_price(column, axes[num])
for temp in range(len(columns), num_rows * num_columns):
    fig.delaxes(axes[temp])

plt_rp.tight_layout()
plt_rp.show()

Preprocessing¶

Ecoding the data¶

In [145]:
df_rp = pd_rp.get_dummies(df_rp,dtype=float,drop_first=True)

Split the data¶

In [146]:
df_rp.head()
Out[146]:
MSSubClass LotFrontage LotArea OverallQual OverallCond YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 BsmtFinSF2 BsmtUnfSF TotalBsmtSF 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath FullBath HalfBath BedroomAbvGr TotRmsAbvGrd Fireplaces GarageYrBlt GarageCars GarageArea WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch MiscVal MoSold YrSold SalePrice MSZoning_FV MSZoning_RH MSZoning_RL MSZoning_RM Street_Pave Alley_No Alley Acess Alley_Pave LotShape_IR2 LotShape_IR3 LotShape_Reg LandContour_HLS LandContour_Low LandContour_Lvl LotConfig_CulDSac LotConfig_FR2 LotConfig_FR3 LotConfig_Inside LandSlope_Mod LandSlope_Sev Neighborhood_Blueste Neighborhood_BrDale Neighborhood_BrkSide Neighborhood_ClearCr Neighborhood_CollgCr Neighborhood_Crawfor Neighborhood_Edwards Neighborhood_Gilbert Neighborhood_IDOTRR Neighborhood_MeadowV Neighborhood_Mitchel Neighborhood_NAmes Neighborhood_NPkVill Neighborhood_NWAmes Neighborhood_NoRidge Neighborhood_NridgHt Neighborhood_OldTown Neighborhood_SWISU Neighborhood_Sawyer Neighborhood_SawyerW Neighborhood_Somerst Neighborhood_StoneBr Neighborhood_Timber Neighborhood_Veenker Condition1_Feedr Condition1_Norm Condition1_PosA Condition1_PosN Condition1_RRAe Condition1_RRAn Condition1_RRNe Condition1_RRNn Condition2_Norm Condition2_RRAn Condition2_RRNn BldgType_2fmCon BldgType_Duplex BldgType_Twnhs BldgType_TwnhsE HouseStyle_1.5Unf HouseStyle_1Story HouseStyle_2.5Unf HouseStyle_2Story HouseStyle_SFoyer HouseStyle_SLvl RoofStyle_Gable RoofStyle_Gambrel RoofStyle_Hip RoofStyle_Mansard RoofMatl_Roll RoofMatl_Tar&Grv RoofMatl_WdShngl Exterior1st_BrkComm Exterior1st_BrkFace Exterior1st_CemntBd Exterior1st_HdBoard Exterior1st_ImStucc Exterior1st_MetalSd Exterior1st_Plywood Exterior1st_Stucco Exterior1st_VinylSd Exterior1st_Wd Sdng Exterior1st_WdShing Exterior2nd_AsphShn Exterior2nd_Brk Cmn Exterior2nd_BrkFace Exterior2nd_CmentBd Exterior2nd_HdBoard Exterior2nd_ImStucc Exterior2nd_MetalSd Exterior2nd_Other Exterior2nd_Plywood Exterior2nd_Stone Exterior2nd_Stucco Exterior2nd_VinylSd Exterior2nd_Wd Sdng Exterior2nd_Wd Shng ExterQual_Fa ExterQual_Gd ExterQual_TA ExterCond_Gd ExterCond_TA Foundation_CBlock Foundation_PConc Foundation_Slab Foundation_Stone Foundation_Wood BsmtQual_Fa BsmtQual_Gd BsmtQual_No Basment BsmtQual_TA BsmtCond_Gd BsmtCond_No Basment BsmtCond_TA BsmtExposure_Gd BsmtExposure_Mn BsmtExposure_No BsmtExposure_No Basment BsmtFinType1_BLQ BsmtFinType1_GLQ BsmtFinType1_LwQ BsmtFinType1_No Basment BsmtFinType1_Rec BsmtFinType1_Unf BsmtFinType2_BLQ BsmtFinType2_GLQ BsmtFinType2_LwQ BsmtFinType2_No Basment BsmtFinType2_Rec BsmtFinType2_Unf Heating_GasA Heating_GasW Heating_Grav Heating_Wall HeatingQC_Fa HeatingQC_Gd HeatingQC_TA CentralAir_Y Electrical_FuseF Electrical_FuseP Electrical_SBrkr KitchenQual_Fa KitchenQual_Gd KitchenQual_TA Functional_Min1 Functional_Min2 Functional_Mod Functional_Typ FireplaceQu_Fa FireplaceQu_Gd FireplaceQu_No Fireplace FireplaceQu_Po FireplaceQu_TA GarageType_Attchd GarageType_Basment GarageType_BuiltIn GarageType_CarPort GarageType_Detchd GarageFinish_RFn GarageFinish_Unf GarageQual_Fa GarageQual_Gd GarageQual_Po GarageQual_TA GarageCond_Fa GarageCond_Gd GarageCond_Po GarageCond_TA PavedDrive_P PavedDrive_Y Fence_GdWo Fence_MnPrv Fence_MnWw Fence_No Fence MiscFeature_Shed SaleType_CWD SaleType_Con SaleType_ConLD SaleType_ConLI SaleType_ConLw SaleType_New SaleType_Oth SaleType_WD SaleCondition_AdjLand SaleCondition_Alloca SaleCondition_Family SaleCondition_Normal SaleCondition_Partial
Id
1 60 65.0 8450 7 5 2003 2003 196.0 706 0 150 856 856 854 0 1710 1 2 1 3 8 0 2003.0 2 548 0 61 0 0 0 0 2 2008 208500 0.0 0.0 1.0 0.0 1.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0
2 20 80.0 9600 6 8 1976 1976 0.0 978 0 284 1262 1262 0 0 1262 0 2 0 3 6 1 1976.0 2 460 298 0 0 0 0 0 5 2007 181500 0.0 0.0 1.0 0.0 1.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0
3 60 68.0 11250 7 5 2001 2002 162.0 486 0 434 920 920 866 0 1786 1 2 1 3 6 1 2001.0 2 608 0 42 0 0 0 0 9 2008 223500 0.0 0.0 1.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0
5 60 84.0 14260 8 5 2000 2000 350.0 655 0 490 1145 1145 1053 0 2198 1 2 1 4 9 1 2000.0 3 836 192 84 0 0 0 0 12 2008 250000 0.0 0.0 1.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0
7 20 75.0 10084 8 5 2004 2005 186.0 1369 0 317 1686 1694 0 0 1694 1 2 0 3 7 1 2004.0 2 636 255 57 0 0 0 0 8 2007 307000 0.0 0.0 1.0 0.0 1.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0
In [147]:
target_col = df_rp.columns.get_loc('SalePrice')
X = df_rp.iloc[:, [col_rp for col_rp in range(df_rp.shape[1]) if col_rp != target_col]].values
y = df_rp.iloc[:,target_col].values

Apply Standardization on the data¶

In [148]:
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=256)
In [149]:
X_train_nstd = X_train.copy()
X_test_nstd = X_test.copy()
y_train_nstd = y_train.copy()

X_train_xg= X_train.copy()
X_test_xg=X_test.copy()
y_train_xg= y_train.copy()
y_test_xg=y_test.copy()

sc_X = StandardScaler()
X_train[:, :34] = sc_X.fit_transform(X_train[:, :34])
X_test[:, :34] = sc_X.transform(X_test[:, :34])
sc_y = StandardScaler()
y_train = sc_y.fit_transform(y_train.reshape(-1,1)).flatten()

Multi-Linear Regression¶

Train the model¶

In [150]:
multi_lr_rp = LinearRegression()
multi_lr_rp.fit(X_train_nstd, y_train_nstd)
Out[150]:
LinearRegression()
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LinearRegression()
In [151]:
# Printing Training and Testing sets R2 and MSE Scores
def print_scores(r2_train_rp, r2_test_rp, mse_train_rp, mse_test_rp, algo_rp):
    print("R2 Train Score using {}: {}".format(algo_rp, r2_train_rp))
    print("R2 Test Score using {} : {}".format(algo_rp, r2_test_rp))
    print("Mean Squared Error of Train using {} : {}".format(algo_rp, mse_train_rp))
    print("Mean Squared Error of Test using {} : {}".format(algo_rp, mse_test_rp))
In [152]:
y_pred = multi_lr_rp.predict(X_test_nstd)
y_train_pred = multi_lr_rp.predict(X_train_nstd)
r2_lr_train_rp = r2_score(y_train_nstd, y_train_pred)
r2_lr_test_rp = r2_score(y_test, y_pred.reshape(-1,1))
mse_lr_train_rp = mean_squared_error(y_train_nstd, y_train_pred)
mse_lr_test_rp = mean_squared_error(y_test, y_pred.reshape(-1,1))

print_scores(r2_lr_train_rp,r2_lr_test_rp,mse_lr_train_rp,mse_lr_test_rp,"Linear Regression")

R2 Train Score using Linear Regression: 0.9491941900302114
R2 Test Score using Linear Regression : 0.8967704699231831
Mean Squared Error of Train using Linear Regression : 165463735.30246514
Mean Squared Error of Test using Linear Regression : 352617475.9582878

Polynomial Regression¶

Training the model¶

In [153]:
poly_reg_rp = PolynomialFeatures(degree = 2)
X_train_poly = poly_reg_rp.fit_transform(X_train)
X_test_poly = poly_reg_rp.transform(X_test)
poly_lr_rp = LinearRegression()
poly_lr_rp.fit(X_train_poly, y_train)
Out[153]:
LinearRegression()
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LinearRegression()

Evaluation the model¶

In [154]:
y_pred = poly_lr_rp.predict(X_test_poly)
y_train_pred = poly_lr_rp.predict(X_train_poly)
r2_poly_train_rp = r2_score(y_train,y_train_pred)
r2_poly_test_rp = r2_score(y_test,sc_y.inverse_transform(y_pred.reshape(-1,1)))
mse_poly_train_rp = mean_squared_error(y_train, y_train_pred)
mse_poly_test_rp = mean_squared_error(y_test, sc_y.inverse_transform(y_pred.reshape(-1,1)))

print_scores(r2_poly_train_rp, r2_poly_test_rp, mse_poly_train_rp, mse_poly_test_rp, "Polynomial Regression")

R2 Train Score using Polynomial Regression: 1.0
R2 Test Score using Polynomial Regression : 0.8253451949027779
Mean Squared Error of Train using Polynomial Regression : 8.631847175963858e-28
Mean Squared Error of Test using Polynomial Regression : 596596114.4213339

Random Forest¶

Training the model¶

RF with estimators = 75¶

In [155]:
rdm_frst_rps_75 = RandomForestRegressor(n_estimators = 75)
rdm_frst_rps_75.fit(X_train, y_train)
Out[155]:
RandomForestRegressor(n_estimators=75)
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RandomForestRegressor(n_estimators=75)

RF with estimators = 100¶

In [156]:
rdm_frst_rps_100 = RandomForestRegressor(n_estimators = 100)
rdm_frst_rps_100.fit(X_train, y_train)
Out[156]:
RandomForestRegressor()
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RandomForestRegressor()

RF with estimators = 125¶

In [157]:
rdm_frst_rps_125 = RandomForestRegressor(n_estimators = 125)
rdm_frst_rps_125.fit(X_train, y_train)
Out[157]:
RandomForestRegressor(n_estimators=125)
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RandomForestRegressor(n_estimators=125)

Random Forest¶

Evaluation the model¶

In [158]:
y_pred = rdm_frst_rps_100.predict(X_test)
y_train_pred = rdm_frst_rps_100.predict(X_train)
r2_frst_train_rp = r2_score(y_train, y_train_pred)
r2_frst_test_rp = r2_score(y_test, sc_y.inverse_transform(y_pred.reshape(-1,1)))
mse_frst_train_rp= mean_squared_error(y_train, y_train_pred)
mse_frst_test_rp = mean_squared_error(y_test, sc_y.inverse_transform(y_pred.reshape(-1,1)))

print_scores(r2_frst_train_rp, r2_frst_test_rp, mse_frst_train_rp, mse_frst_test_rp, "Random Forest Regression (100)")

R2 Train Score using Random Forest Regression (100): 0.9834267646891539
R2 Test Score using Random Forest Regression (100) : 0.876744889688229
Mean Squared Error of Train using Random Forest Regression (100) : 0.016573235310846136
Mean Squared Error of Test using Random Forest Regression (100) : 421022026.010924
In [159]:
y_pred = rdm_frst_rps_75.predict(X_test)
y_train_pred = rdm_frst_rps_75.predict(X_train)
r2_frst_train_75 = r2_score(y_train, y_train_pred)
r2_frst_test_75 = r2_score(y_test, sc_y.inverse_transform(y_pred.reshape(-1,1)))
mse_frst_train_75 = mean_squared_error(y_train, y_train_pred)
mse_frst_test_75 = mean_squared_error(y_test, sc_y.inverse_transform(y_pred.reshape(-1,1)))

print_scores(r2_frst_train_75, r2_frst_test_75, mse_frst_train_75, mse_frst_test_75, "Random Forest Regression (75)")

R2 Train Score using Random Forest Regression (75): 0.9826005742915324
R2 Test Score using Random Forest Regression (75) : 0.8805141613357595
Mean Squared Error of Train using Random Forest Regression (75) : 0.01739942570846763
Mean Squared Error of Test using Random Forest Regression (75) : 408146727.1156918
In [160]:
y_pred = rdm_frst_rps_125.predict(X_test)
y_train_pred = rdm_frst_rps_125.predict(X_train)
r2_frst_train_rp = r2_score(y_train, y_train_pred)
r2_frst_test_rp = r2_score(y_test, sc_y.inverse_transform(y_pred.reshape(-1,1)))
mse_frst_train_rp = mean_squared_error(y_train, y_train_pred)
mse_frst_test_rp = mean_squared_error(y_test, sc_y.inverse_transform(y_pred.reshape(-1,1)))

print_scores(r2_frst_train_rp, r2_frst_test_rp, mse_frst_train_rp, mse_frst_test_rp, "Random Forest Regression (125)")

R2 Train Score using Random Forest Regression (125): 0.9834972738384505
R2 Test Score using Random Forest Regression (125) : 0.878074760293716
Mean Squared Error of Train using Random Forest Regression (125) : 0.01650272616154943
Mean Squared Error of Test using Random Forest Regression (125) : 416479376.09370524

We have altered the n_estimators in the Random Forest and hence we can see that it gives best result with any value out of [75,100,125]¶

Linear SVR¶

Training the model¶

In [161]:
svr_lrp_rs = SVR(kernel = 'linear')
svr_lrp_rs.fit(X_train, y_train)
Out[161]:
SVR(kernel='linear')
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SVR(kernel='linear')

Evaluation the model¶

In [162]:
y_pred = svr_lrp_rs.predict(X_test)
y_train_pred = svr_lrp_rs.predict(X_train)
r2_svr_lr_train_rp = r2_score(y_train, y_train_pred)
r2_svr_lr_test_rp = r2_score(y_test, sc_y.inverse_transform(y_pred.reshape(-1,1)))
mse_svr_lr_train_rp = mean_squared_error(y_train, y_train_pred)
mse_svr_lr_test_rp = mean_squared_error(y_test, sc_y.inverse_transform(y_pred.reshape(-1,1)))

print_scores(r2_svr_lr_train_rp, r2_svr_lr_test_rp, mse_svr_lr_train_rp, mse_svr_lr_test_rp, "Support Vector Regression")

R2 Train Score using Support Vector Regression: 0.9439305022854688
R2 Test Score using Support Vector Regression : 0.9138355058165617
Mean Squared Error of Train using Support Vector Regression : 0.05606949771453119
Mean Squared Error of Test using Support Vector Regression : 294325726.69445837

SVR using Radial Basis Function¶

In [163]:
svr_rbf_rp = SVR(kernel = 'rbf')
svr_rbf_rp.fit(X_train, y_train)
Out[163]:
SVR()
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SVR()

Evaluation the model¶

In [164]:
y_pred = svr_rbf_rp.predict(X_test)
y_train_pred = svr_rbf_rp.predict(X_train)
r2_svr_train_rp = r2_score(y_train, y_train_pred)
r2_svr_test_rp = r2_score(y_test, sc_y.inverse_transform(y_pred.reshape(-1,1)))
mse_svr_train_rp = mean_squared_error(y_train, y_train_pred)
mse_svr_test_rp = mean_squared_error(y_test, sc_y.inverse_transform(y_pred.reshape(-1,1)))

print_scores(r2_svr_train_rp, r2_svr_test_rp, mse_svr_train_rp, mse_svr_test_rp, "Support Vector Regression_rbf")

R2 Train Score using Support Vector Regression_rbf: 0.9677502574058094
R2 Test Score using Support Vector Regression_rbf : 0.9005401855006185
Mean Squared Error of Train using Support Vector Regression_rbf : 0.032249742594190514
Mean Squared Error of Test using Support Vector Regression_rbf : 339740660.6612818

Here we have changed the kernel from linear to RBF and hence found a significant increase in the model performance¶

In [165]:
from xgboost import XGBRegressor
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.metrics import mean_squared_error

param_grid_rs = {
    'learning_rate': [ 0.1, 0.2, 0.3, 0.4],
    'gamma': [0.01, 0.1, 0.2, 0.3, 0.4]
}
xgb = XGBRegressor()

grid_search_rp = GridSearchCV(estimator=xgb, param_grid=param_grid_rs, scoring='neg_mean_squared_error', cv=5)
grid_search_rp.fit(X_train_xg, y_train_xg)

best_params = grid_search_rp.best_params_
print("Best Hyperparameters:", best_params)
y_pred = grid_search.best_estimator_.predict(X_test_xg)

Best Hyperparameters: {'gamma': 0.01, 'learning_rate': 0.1}

XGBoost Regressor¶

Training the model¶

We can clearly see that out of the given values, we have tuned the model and found the best combination as gamma(0.01) and learning rate(0.1)¶

In [166]:
xgb_rp = XGBRegressor(learning_rate=0.1,gamma=0.01)
xgb_rp.fit(X_train, y_train)
Out[166]:
XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=0.01, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=0.1, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             multi_strategy=None, n_estimators=None, n_jobs=None,
             num_parallel_tree=None, random_state=None, ...)
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XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=0.01, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=0.1, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             multi_strategy=None, n_estimators=None, n_jobs=None,
             num_parallel_tree=None, random_state=None, ...)
In [167]:
y_pred = xgb_rp.predict(X_test)
y_train_pred = xgb_rp.predict(X_train)
r2_xgb_train_rp = r2_score(y_train, y_train_pred)
r2_xgb_test_rp = r2_score(y_test, sc_y.inverse_transform(y_pred.reshape(-1,1)))
mse_xgb_train_rp = mean_squared_error(y_train, y_train_pred)
mse_xgb_test_rp = mean_squared_error(y_test, sc_y.inverse_transform(y_pred.reshape(-1,1)))

print_scores(r2_xgb_train_rp, r2_xgb_test_rp, mse_xgb_train_rp, mse_xgb_test_rp, "XGBoost")

R2 Train Score using XGBoost: 0.9960759723139573
R2 Test Score using XGBoost : 0.8813379856508698
Mean Squared Error of Train using XGBoost : 0.00392402768604268
Mean Squared Error of Test using XGBoost : 405332659.76102

K-NN Model¶

Creating and train the model¶

In [168]:
from sklearn.neighbors import KNeighborsRegressor

knn = KNeighborsRegressor(n_neighbors=5)
knn.fit(X_train, y_train)

knn_test = KNeighborsRegressor(n_neighbors=500)
knn_test.fit(X_train, y_train)

y_pred = knn.predict(X_test)
y_train_pred = knn.predict(X_train)

r2_knn_train_rp = r2_score(y_train, y_train_pred)
r2_knn_test_rp = r2_score(y_test, y_pred)
mse_knn_train_rp = mean_squared_error(y_train, y_train_pred)
mse_knn_test_rp = mean_squared_error(y_test, y_pred)

print_scores(r2_knn_train_rp, r2_knn_test_rp, mse_knn_train_rp, mse_knn_test_rp , "KNN")

R2 Train Score using KNN: 0.8902326710346942
R2 Test Score using KNN : -8.504651413383005
Mean Squared Error of Train using KNN : 0.10976732896530578
Mean Squared Error of Test using KNN : 32466545074.42308
In [169]:
y_pred = knn_test.predict(X_test)
y_train_pred = knn_test.predict(X_train)
r2_knn_train_rp = r2_score(y_train, y_train_pred)
r2_knn_test_rp = r2_score(y_test, y_pred)
mse_knn_train_rp = mean_squared_error(y_train, y_train_pred)
mse_knn_test_rp = mean_squared_error(y_test, y_pred)

print_scores(r2_knn_train_rp, r2_knn_test_rp, mse_knn_train_rp, mse_knn_test_rp , "KNN(500)")

R2 Train Score using KNN(500): 0.4666241814351054
R2 Test Score using KNN(500) : -8.504667145091746
Mean Squared Error of Train using KNN(500) : 0.5333758185648946
Mean Squared Error of Test using KNN(500) : 32466598811.715347

Here we can see that when the neighbours are more i.e. 500 we are getting model with less performance as compared to model which has k=5.¶

In [170]:
models = pd_rp.DataFrame({
    'Model': [
        'Multiple Linear Regression','Polynomial Regression',
        'Random Forest', 'Support Vector Regression','Linear Support Vector Regression','XGBoost','KNN'
    ],
    'Training R2 Score': [
        r2_lr_train_rp,r2_poly_train_rp,r2_frst_train_75,r2_svr_train_rp,r2_svr_lr_train_rp,r2_xgb_train_rp, r2_knn_train_rp
    ],
    'Training Mean Square Error': [
       mse_lr_train_rp,mse_poly_train_rp,mse_frst_train_75,mse_svr_train_rp,mse_svr_lr_train_rp,mse_xgb_train_rp, mse_knn_train_rp
    ],
    'Testing R2 Score': [
        r2_lr_test_rp,r2_poly_test_rp,r2_frst_test_75,r2_svr_test_rp,r2_svr_lr_test_rp,r2_xgb_test_rp , r2_knn_test_rp
    ],
    'Testing Mean Square Error': [
       mse_lr_test_rp,mse_poly_test_rp,mse_frst_test_75,mse_svr_test_rp,mse_svr_lr_test_rp,mse_xgb_test_rp, mse_knn_test_rp
    ]
})
In [171]:
models.sort_values(by='Testing R2 Score', ascending=False).style.background_gradient(
        cmap='Blues')
Out[171]:
  Model Training R2 Score Training Mean Square Error Testing R2 Score Testing Mean Square Error
4 Linear Support Vector Regression 0.943931 0.056069 0.913836 294325726.694458
3 Support Vector Regression 0.967750 0.032250 0.900540 339740660.661282
0 Multiple Linear Regression 0.949194 165463735.302465 0.896770 352617475.958288
5 XGBoost 0.996076 0.003924 0.881338 405332659.761020
2 Random Forest 0.982601 0.017399 0.880514 408146727.115692
1 Polynomial Regression 1.000000 0.000000 0.825345 596596114.421334
6 KNN 0.466624 0.533376 -8.504667 32466598811.715347
In [172]:
y_pred = svr_lrp_rs.predict(X_test)
y_train_pred = svr_lrp_rs.predict(X_train)
r2_svr_lr_train_rp = r2_score(y_train, y_train_pred)
r2_svr_lr_test_rp = r2_score(y_test, sc_y.inverse_transform(y_pred.reshape(-1,1)))
mse_svr_lr_train_rp = mean_squared_error(y_train, y_train_pred)
mse_svr_lr_test_rp = mean_squared_error(y_test, sc_y.inverse_transform(y_pred.reshape(-1,1)))

print_scores(r2_svr_lr_train_rp, r2_svr_lr_test_rp, mse_svr_lr_train_rp, mse_svr_lr_test_rp, "Support Vector Regression")

ids = np.arange(1, len(y_test) + 1)
csv_file_path = "model_scores.csv"

with open(csv_file_path, mode='a', newline='') as file:
    writer = csv.writer(file)
    
    for i, saleprice in zip(ids, sc_y.inverse_transform(y_pred.reshape(-1,1))):
        writer.writerow([i, saleprice[0]])
    
print(f"Scores saved to {csv_file_path}")

R2 Train Score using Support Vector Regression: 0.9439305022854688
R2 Test Score using Support Vector Regression : 0.9138355058165617
Mean Squared Error of Train using Support Vector Regression : 0.05606949771453119
Mean Squared Error of Test using Support Vector Regression : 294325726.69445837
Scores saved to model_scores.csv
In [ ]: